Preoperative nomogram for predicting spread through air spaces in clinical-stage IA non-small cell lung cancer using 18F-fluorodeoxyglucose positron emission tomography/computed tomography.

PURPOSE: This study aims to assess the predictive value of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) radiological features and the maximum standardized uptake value (SUVmax) in determining the presence of spread through air spaces (STAS) in clinical-stage IA non-small cell lung cancer (NSCLC). METHODS: A retrospective analysis was conducted on 180 cases of NSCLC with postoperative pathological assessment of STAS status, spanning from September 2019 to September 2023. Of these, 116 cases from hospital one comprised the training set, while 64 cases from hospital two formed the testing set. The clinical information, tumor SUVmax, and 13 related CT features were analyzed. Subgroup analysis was carried out based on tumor density type. In the training set, univariable and multivariable logistic regression analyses were employed to identify the most significant variables. A multivariable logistic regression model was constructed and the corresponding nomogram was developed to predict STAS in NSCLC, and its diagnostic efficacy was evaluated in the testing set. RESULTS: SUVmax, consolidation-to-tumor ratio (CTR), and lobulation sign emerged as the best combination of variables for predicting STAS in NSCLC. Among these, SUVmax and CTR were identified as independent predictors for STAS prediction. The constructed prediction model demonstrated area under the curve (AUC) values of 0.796 and 0.821 in the training and testing sets, respectively. Subgroup analysis revealed a 2.69 times higher STAS-positive rate in solid nodules compared to part-solid nodules. SUVmax was an independent predictor for predicting STAS in solid nodular NSCLC, while CTR and an emphysema background were independent predictors for STAS in part-solid nodular NSCLC. CONCLUSION: Our nomogram based on preoperative 18F-FDG PET/CT radiological features and SUVmax effectively predicts STAS status in clinical-stage IA NSCLC. Furthermore, our study highlights that metabolic parameters and CT variables associated with STAS differ between solid and part-solid nodular NSCLC.

CT-Based Intratumoral and Peritumoral Radiomics Nomograms for the Preoperative Prediction of Spread Through Air Spaces in Clinical Stage IA Non-small Cell Lung Cancer.

The study aims to investigate the value of intratumoral and peritumoral radiomics and clinical-radiological features for predicting spread through air spaces (STAS) in patients with clinical stage IA non-small cell lung cancer (NSCLC). A total of 336 NSCLC patients from our hospital were randomly divided into the training cohort (n = 236) and the internal validation cohort (n = 100) at a ratio of 7:3, and 69 patients from the other two external hospitals were collected as the external validation cohort. Univariate and multivariate analyses were used to select clinical-radiological features and construct a clinical model. The GTV, PTV5, PTV10, PTV15, PTV20, GPTV5, GPTV10, GPTV15, and GPTV20 models were constructed based on intratumoral and peritumoral (5 mm, 10 mm, 15 mm, 20 mm) radiomics features. Additionally, the radscore of the optimal radiomics model and clinical-radiological predictors were used to construct a combined model and plot a nomogram. Lastly, the ROC curve and AUC value were used to evaluate the diagnostic performance of the model. Tumor density type (OR = 6.738) and distal ribbon sign (OR = 5.141) were independent risk factors for the occurrence of STAS. The GPTV10 model outperformed the other radiomics models, and its AUC values were 0.887, 0.876, and 0.868 in the three cohorts. The AUC values of the combined model constructed based on GPTV10 radscore and clinical-radiological predictors were 0.901, 0.875, and 0.878. DeLong test results revealed that the combined model was superior to the clinical model in the three cohorts. The nomogram based on GPTV10 radscore and clinical-radiological features exhibited high predictive efficiency for STAS status in NSCLC.

CT whole lung radiomic nomogram: a potential biomarker for lung function evaluation and identification of COPD.

BACKGROUND: Computed tomography (CT) plays a great role in characterizing and quantifying changes in lung structure and function of chronic obstructive pulmonary disease (COPD). This study aimed to explore the performance of CT-based whole lung radiomic in discriminating COPD patients and non-COPD patients. METHODS: This retrospective study was performed on 2785 patients who underwent pulmonary function examination in 5 hospitals and were divided into non-COPD group and COPD group. The radiomic features of the whole lung volume were extracted. Least absolute shrinkage and selection operator (LASSO) logistic regression was applied for feature selection and radiomic signature construction. A radiomic nomogram was established by combining the radiomic score and clinical factors. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were used to evaluate the predictive performance of the radiomic nomogram in the training, internal validation, and independent external validation cohorts. RESULTS: Eighteen radiomic features were collected from the whole lung volume to construct a radiomic model. The area under the curve (AUC) of the radiomic model in the training, internal, and independent external validation cohorts were 0.888 [95% confidence interval (CI) 0.869-0.906], 0.874 (95%CI 0.844-0.904) and 0.846 (95%CI 0.822-0.870), respectively. All were higher than the clinical model (AUC were 0.732, 0.714, and 0.777, respectively, P < 0.001). DCA demonstrated that the nomogram constructed by combining radiomic score, age, sex, height, and smoking status was superior to the clinical factor model. CONCLUSIONS: The intuitive nomogram constructed by CT-based whole-lung radiomic has shown good performance and high accuracy in identifying COPD in this multicenter study.

CT-based whole lung radiomics nomogram: a tool for identifying the risk of cardiovascular disease in patients with chronic obstructive pulmonary disease.

OBJECTIVES: To evaluate the value of CT-based whole lung radiomics nomogram for identifying the risk of cardiovascular disease (CVD) in patients with chronic obstructive pulmonary disease (COPD). MATERIALS AND METHODS: A total of 974 patients with COPD were divided into a training cohort (n = 402), an internal validation cohort (n = 172), and an external validation cohort (n = 400) from three hospitals. Clinical data and CT findings were analyzed. Radiomics features of whole lung were extracted from the non-contrast chest CT images. A radiomics signature was constructed with algorithms. Combined with the radiomics score and independent clinical factors, multivariate logistic regression analysis was used to establish a radiomics nomogram. ROC curve was used to analyze the prediction performance of the model. RESULTS: Age, weight, and GOLD were the independent clinical factors. A total of 1218 features were extracted and reduced to 15 features to build the radiomics signature. In the training cohort, the combined model (area under the curve [AUC], 0.731) showed better discrimination capability (p < 0.001) than the clinical factors model (AUC, 0.605). In the internal validation cohort, the combined model (AUC, 0.727) performed better (p = 0.032) than the clinical factors model (AUC, 0.629). In the external validation cohort, the combined model (AUC, 0.725) performed better (p < 0.001) than the clinical factors model (AUC, 0.690). Decision curve analysis demonstrated the radiomics nomogram outperformed the clinical factors model. CONCLUSION: The CT-based whole lung radiomics nomogram has the potential to identify the risk of CVD in patients with COPD. CLINICAL RELEVANCE STATEMENT: This study helps to identify cardiovascular disease risk in patients with chronic obstructive pulmonary disease on chest CT scans. KEY POINTS: • To investigate the value of CT-based whole lung radiomics features in identifying the risk of cardiovascular disease in chronic obstructive pulmonary disease patients. • The radiomics nomogram showed better performance than the clinical factors model to identify the risk of cardiovascular disease in patients with chronic obstructive pulmonary disease. • The radiomics nomogram demonstrated excellent performance in the training, internal validation, and external validation cohort (AUC, 0.731; AUC, 0.727; AUC, 0.725).

Extracellular volume fraction as a potential predictor to differentiate lung cancer from benign lung lesions with dual-layer detector spectral CT.

Background: Extracellular volume (ECV) fraction has been used in cardiovascular diseases, pancreatic fibrosis, and hepatic fibrosis. The diagnostic value of ECV for focal lung lesions remains to be explored. The aim of this study was to evaluate the feasibility of ECV derived from a dual-layer detector computed tomography (DLCT) to differentiate lung cancer (LC) from benign lung lesions (BLLs). Methods: Retrospectively, 128 consecutive patients with pathologically confirmed LC (n=86) or BLLs (n=42) were included. Conventional computed tomography (CT) characteristics and spectral CT parameters were assessed. All patients' hematocrits were measured to correct contrast volume distributions in blood while calculating ECV. After performing logistic regression analysis, a conventional CT-based model (Model A), DLCT-based model (Model B), combined diagnostic models (Model C), and an ECV-based model (Model D) were developed. The diagnostic effectiveness of each model was examined using the receiver operating characteristic (ROC) curve and their corresponding 95% confidence intervals (CIs). The area under the curve (AUC) of each model was compared using the DeLong test. Results: Certain conventional CT features (such as lesion size, lobulation, spiculation, pleural indentation, and enlarged lymph nodes) differed significantly between the LC and BLL groups (all P<0.05). Statistical differences were found in the following DLCT parameters (all P<0.05): effective atomic number (Zeff) (non-enhancement), electron density (ED) (non-enhancement), ECV, iodine concentration (IC), and normalized iodine concentration (NIC). Models A, B, C, and D had AUCs of 0.801 [95% confidence interval (CI): 0.721-0.866], 0.805 (95% CI: 0.726-0.870), 0.925 (95% CI: 0.865-0.964), and 0.754 (95% CI: 0.671-0.826), respectively. The AUC of Model D (ECV) showed no significant difference from that of Models A and B (DeLong test, P>0.05). Conclusions: The ECV derived from DLCT may be a potential new method to differentiate LC from BLLs, broadening the scope of ECV in clinical research.

Baseline whole-lung CT features deriving from deep learning and radiomics: prediction of benign and malignant pulmonary ground-glass nodules.

Objective: To develop and validate the model for predicting benign and malignant ground-glass nodules (GGNs) based on the whole-lung baseline CT features deriving from deep learning and radiomics. Methods: This retrospective study included 385 GGNs from 3 hospitals, confirmed by pathology. We used 239 GGNs from Hospital 1 as the training and internal validation set; 115 and 31 GGNs from Hospital 2 and Hospital 3 as the external test sets 1 and 2, respectively. An additional 32 stable GGNs from Hospital 3 with more than five years of follow-up were used as the external test set 3. We evaluated clinical and morphological features of GGNs at baseline chest CT and extracted the whole-lung radiomics features simultaneously. Besides, baseline whole-lung CT image features are further assisted and extracted using the convolutional neural network. We used the back-propagation neural network to construct five prediction models based on different collocations of the features used for training. The area under the receiver operator characteristic curve (AUC) was used to compare the prediction performance among the five models. The Delong test was used to compare the differences in AUC between models pairwise. Results: The model integrated clinical-morphological features, whole-lung radiomic features, and whole-lung image features (CMRI) performed best among the five models, and achieved the highest AUC in the internal validation set, external test set 1, and external test set 2, which were 0.886 (95% CI: 0.841-0.921), 0.830 (95%CI: 0.749-0.893) and 0.879 (95%CI: 0.712-0.968), respectively. In the above three sets, the differences in AUC between the CMRI model and other models were significant (all P < 0.05). Moreover, the accuracy of the CMRI model in the external test set 3 was 96.88%. Conclusion: The baseline whole-lung CT features were feasible to predict the benign and malignant of GGNs, which is helpful for more refined management of GGNs.

Ultra-low-dose spectral-detector computed tomography for the accurate quantification of pulmonary nodules: an anthropomorphic chest phantom study.

PURPOSE: To assess the quantification accuracy of pulmonary nodules using virtual monoenergetic images (VMIs) derived from spectral-detector computed tomography (CT) under an ultra-low-dose scan protocol. METHODS: A chest phantom consisting of 12 pulmonary nodules was scanned using spectral-detector CT at 100 kVp/10 mAs, 100 kVp/20 mAs, 120 kVp/10 mAs, and 120 kVp/30 mAs. Each scanning protocol was repeated three times. Each CT scan was reconstructed utilizing filtered back projection, hybrid iterative reconstruction, iterative model reconstruction (IMR), and VMIs of 40-100 keV. The signal-to-noise ratio and air noise of images, absolute differences, and absolute percentage measurement errors (APEs) of the diameter, density, and volume of the four scan protocols and ten reconstruction images were compared. RESULTS: With each fixed reconstruction image, the four scanning protocols exhibited no significant differences in APEs for diameter and density (all P > 0.05). Of the four scan protocols and ten reconstruction images, APEs for nodule volume had no significant differences (all P > 0.05). At 100 kVp/10 mAs, APEs for density using IMR were the lowest (APE-mean: 6.69), but no significant difference was detected between VMIs at 50 keV (APE-mean: 11.69) and IMR (P = 0.666). In the subgroup analysis, at 100 kVp/10 mAs, there were no significant differences between VMIs at 50 keV and IMR in diameter and density (all P > 0.05). The radiation dose at 100 kVp/10 mAs was reduced by 77.8% compared with that at 120 kVp/30 mAs. CONCLUSION: Compared with IMR, reconstruction at 100 kVp/10 mAs and 50 keV provides a more accurate quantification of pulmonary nodules, and the radiation dose is reduced by 77.8% compared with that at 120 kVp/30 mAs, demonstrating great potential for ultra-low-dose spectral-detector CT.

Radiomics-based Machine Learning Methods for Volume Doubling Time Prediction of Pulmonary Ground-glass Nodules With Baseline Chest Computed Tomography.

PURPOSE: Reliable prediction of volume doubling time (VDT) is essential for the personalized management of pulmonary ground-glass nodules (GGNs). We aimed to determine the optimal VDT prediction method by comparing different machine learning methods only based on the baseline chest computed tomography (CT) images. MATERIALS AND METHODS: Seven classical machine learning methods were evaluated in terms of their stability and performance for VDT prediction. The VDT, calculated by the preoperative and baseline CT, was divided into 2 groups with a cutoff value of 400 days. A total of 90 GGNs from 3 hospitals constituted the training set, and 86 GGNs from the fourth hospital served as the external validation set. The training set was used for feature selection and model training, and the validation set was used to evaluate the predictive performance of the model independently. RESULTS: The eXtreme Gradient Boosting showed the highest predictive performance (accuracy: 0.890±0.128 and area under the ROC curve (AUC): 0.896±0.134), followed by the neural network (NNet) (accuracy: 0.865±0.103 and AUC: 0.886±0.097). While regarding stability, the NNet showed the highest robustness against data perturbation (relative SDs [%] of mean AUC: 10.9%). Therefore, the NNet was chosen as the final model, achieving high accuracy of 0.756 in the external validation set. CONCLUSION: The NNet is a promising machine learning method to predict the VDT of GGNs, which would assist in the personalized follow-up and treatment strategies for GGNs reducing unnecessary follow-up and radiation dose.

Nomograms Using CT Morphological Features and Clinical Characteristics to Identify COPD in Patients with Lung Cancer: A Multicenter Study.

Purpose: This study aimed to screen out computed tomography (CT) morphological features and clinical characteristics of patients with lung cancer to identify chronic obstructive pulmonary disease (COPD). Further, we aimed to develop and validate different diagnostic nomograms for predicting whether lung cancer is comorbid with COPD. Patients and Methods: This retrospective study examined data from 498 patients with lung cancer (280 with COPD, 218 without COPD; 349 in training cohort, 149 in validation cohort) from two centers. Five clinical characteristics and 20 CT morphological features were evaluated. Differences in all variables were assessed between COPD and non-COPD groups. Models were developed using multivariable logistic regression to identify COPD, including clinical, imaging, and combined nomograms. Receiver operating characteristic curves were used to evaluate and compare the performance of nomograms. Results: Age, sex, interface, bronchus cutoff sign, spine-like process, and spiculation sign were independent predictors of COPD in patients with lung cancer. In the training and validation cohorts, the clinical nomogram showed good performance to predict COPD in lung cancer patients (areas under the curves [AUCs] of 0.807 [95% CI, 0.761-0.854] and 0.753 [95% CI, 0.674-0.832]); while the imaging nomogram showed slightly better performance (AUCs of 0.814 [95% CI, 0.770-0.858] and 0.780 [95% CI, 0.705-0.856]). For the combined nomogram generated with clinical and imaging features, the performance was further improved (AUC=0.863 [95% CI, 0.824-0.903], 0.811 [95% CI, 0.742-0.880] in the training and validation cohort). At 60% risk threshold, there were more true negative predictions (48 vs 44) and higher accuracy (73.15% vs 71.14%) for the combined nomogram compared with the clinical nomogram in the validation cohort. Conclusion: The combined nomogram developed with clinical and imaging features outperformed clinical and imaging nomograms; this provides a convenient method to detect COPD in patients with lung cancer using one-stop CT scanning.

CT-Based Radiomic Nomogram for the Prediction of Chronic Obstructive Pulmonary Disease in Patients with Lung cancer.

RATIONALE AND OBJECTIVES: To develop and validate a model for predicting chronic obstructive pulmonary disease (COPD) in patients with lung cancer based on computed tomography (CT) radiomic signatures and clinical and imaging features. MATERIALS AND METHODS: We retrospectively enrolled 443 patients with lung cancer who underwent pulmonary function test as the primary cohort. They were randomly assigned to the training (n = 311) or validation (n = 132) set in a 7:3 ratio. Additionally, an independent external cohort of 54 patients was evaluated. The radiomic lung nodule signature was constructed using the least absolute shrinkage and selection operator algorithm, while key variables were selected using logistic regression to develop the clinical and combined models presented as a nomogram. RESULTS: COPD was significantly related to the radiomics signature in both cohorts. Moreover, the signature served as an independent predictor of COPD in the multivariate regression analysis. For the training, internal, and external cohorts, the area under the receiver operating characteristic curve (ROC, AUC) values of our radiomics signature for COPD prediction were 0.85, 0.85, and 0.76, respectively. Additionally, the AUC values of the radiomic nomogram for COPD prediction were 0.927, 0.879, and 0.762 for the three cohorts, respectively, which outperformed the other two models. CONCLUSION: The present study presents a nomogram that incorporates radiomics signatures and clinical and radiological features, which could be used to predict the risk of COPD in patients with lung cancer with one-stop chest CT scanning.

Multivariate analysis based on the maximum standard unit value of 18F-fluorodeoxyglucose positron emission tomography/computed tomography and computed tomography features for preoperative predicting of visceral pleural invasion in patients with subpleural clinical stage IA peripheral lung adenocarcinoma

PURPOSE: Preoperative prediction of visceral pleural invasion (VPI) is important because it enables thoracic surgeons to choose appropriate surgical plans. This study aimed to develop and validate a multivariate logistic regression model incorporating the maximum standardized uptake value (SUVmax) and valuable computed tomography (CT) signs for the non-invasive prediction of VPI status in subpleural clinical stage IA lung adenocarcinoma patients before surgery. METHODS: A total of 140 patients with subpleural clinical stage IA peripheral lung adenocarcinoma were recruited and divided into a training set (n = 98) and a validation set (n = 42), according to the positron emission tomography/CT examination temporal sequence, with a 7:3 ratio. Next, VPI-positive and VPI-negative groups were formed based on the pathological results. In the training set, the clinical information, the SUVmax, the relationship between the tumor and the pleura, and the CT features were analyzed using univariate analysis. The variables with significant differences were included in the multivariate analysis to construct a prediction model. A nomogram based on multivariate analysis was developed, and its predictive performance was verified in the validation set. RESULTS: The size of the solid component, the consolidation-to-tumor ratio, the solid component pleural contact length, the SUVmax, the density type, the pleural indentation, the spiculation, and the vascular convergence sign demonstrated significant differences between VPI-positive (n = 40) and VPI-negative (n = 58) cases on univariate analysis in the training set. A multivariate logistic regression model incorporated the SUVmax [odds ratio (OR): 1.753, P = 0.002], the solid component pleural contact length (OR: 1.101, P = 0.034), the pleural indentation (OR: 5.075, P = 0.041), and the vascular convergence sign (OR: 13.324, P = 0.025) as the best combination of predictors, which were all independent risk factors for VPI in the training group. The nomogram indicated promising discrimination, with an area under the curve value of 0.892 [95% confidence interval (CI), 0.813-0.946] in the training set and 0.885 (95% CI, 0.748-0.962) in the validation set. The calibration curve demonstrated that its predicted probabilities were in acceptable agreement with the actual probability. The decision curve analysis illustrated that the current nomogram would add more net benefit. CONCLUSION: The nomogram integrating the SUVmax and the CT features could non-invasively predict VPI status before surgery in subpleural clinical stage IA lung adenocarcinoma patients.

YAP via interacting with STAT3 regulates VEGF-induced angiogenesis in human retinal microvascular endothelial cells.

Endothelial dysfunction is a main feature of retinal neovascular diseases which are the leading cause of blindness in developed countries. Yes-associated protein (YAP) and signal transducer and activator of transcription factor 3 (STAT3) participate in angiogenesis via vascular endothelial growth factor (VEGF) signaling. Additionally, YAP can bind STAT3 in endothelial cells. In the study, dimethyloxalylglycine (DMOG) stimulated human retinal microvascular endothelial cells (HRMECs) was used as retinal endothelial hypoxia model. The proliferation of HRMECs, as well as t-YAP, p-STAT3 (Tyr705) increased, while p-YAP (Ser127), p-YAP (Ser397) decreased following hypoxia. Meanwhile, YAP and STAT3 translocated to the nucleus. YAP knockdown inhibited the proliferation, migration and tube formation of HRMECs. YAP overexpression up-regulated phosphorylation of STAT3. The YAP overexpression-induced HRMECs proliferation, migration and tube formation were reversed by S3I-201, a selective STAT3 inhibitor. YAP interacted with STAT3 to promote STAT3 nuclear translocation. Additionally, YAP and STAT3 promoted the transcription of VEGF synergistically. Finally, inhibition of YAP alleviated retinal pathological neovascularization in mouse oxygen-induced retinopathy (OIR) model. In summary, activated YAP interacted with STAT3 to promote the activation and nuclear translocation of STAT3, hence boosted the proliferation, migration and tube formation of HRMECs via VEGF signaling following hypoxia. The data will further elucidate the mechanisms of retinal neovascular diseases.

Inhibition of TRAF6 alleviates choroidal neovascularization in vivo.

Choroidal neovascularization (CNV) is a type of wet age-related macular degeneration (AMD) which is a major cause of blindness in elder patients. Tumor necrosis factor receptor-associated factor 6 (TRAF6) promotes tumor angiogenesis via upregulating the expression of hypoxia-inducible factor 1α (HIF-1α) and vascular endothelial growth factor (VEGF). Additionally, TRAF6 facilitates the inflammatory response in macrophages and microglia. Here, using mouse laser-induced CNV model and TRAF6 siRNA, the study shows that TRAF6 is a critical player in CNV. The expression of TRAF6, HIF-1α, and VEGF increased in the model. TFAF6 siRNA intravitreal (IVT) injection inhibited CNV formation, as well as expression of HIF-1α and VEGF, activation of macrophages and microglia. Together, our data suggest that TFAF6 inhibition reduces CNV formation via down-regulating expression of HIF-1α and VEGF and activation of macrophages and microglia, demonstrating the unique advantages of TRAF6 inhibition in the alleviation of AMD.